Same, Same, but Different: Algorithmic Diversification of Viewpoints in News

Nava Tintarev; Emily Sullivan; Dror Guldin; Sihang Qiu; Daan Odjik

doi:10.1145/3213586.3226203

Same, Same, but Different: Algorithmic Diversification of Viewpoints in News

Nava Tintarev, Emily Sullivan, Dror Guldin, Sihang Qiu, Daan Odjik

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

19 Citations (Scopus)

196 Downloads (Pure)

Abstract

Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.

Original language	English
Title of host publication	UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	7-13
Number of pages	7
ISBN (Print)	978-1-4503-5784-5
DOIs	https://doi.org/10.1145/3213586.3226203
Publication status	Published - 2018
Event	UMAP 2018 : The 26th Conference on User Modeling, Adaptation and Personalization - Singapore, Singapore Duration: 8 Jul 2018 → 11 Jul 2018 Conference number: 26

Conference

Conference	UMAP 2018
Abbreviated title	UMAP '18
Country/Territory	Singapore
City	Singapore
Period	8/07/18 → 11/07/18

Bibliographical note

Accepted author manuscript

Keywords

News recommendation
diversity based ranking
framing

Access to Document

10.1145/3213586.3226203

Same__Same__but_DifferentAccepted author manuscript, 668 KB

Cite this

@inproceedings{fb5a211127e646a89ef8c14036439d9a,

title = "Same, Same, but Different: Algorithmic Diversification of Viewpoints in News",

abstract = "Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.",

keywords = "News recommendation, diversity based ranking, framing",

author = "Nava Tintarev and Emily Sullivan and Dror Guldin and Sihang Qiu and Daan Odjik",

note = "Accepted author manuscript; UMAP 2018 : The 26th Conference on User Modeling, Adaptation and Personalization , UMAP '18 ; Conference date: 08-07-2018 Through 11-07-2018",

year = "2018",

doi = "10.1145/3213586.3226203",

language = "English",

isbn = "978-1-4503-5784-5 ",

pages = "7--13",

booktitle = "UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

}

Tintarev, N, Sullivan, E, Guldin, D, Qiu, S & Odjik, D 2018, Same, Same, but Different: Algorithmic Diversification of Viewpoints in News. in UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), New York, NY, pp. 7-13, UMAP 2018 , Singapore, Singapore, 8/07/18. https://doi.org/10.1145/3213586.3226203

Same, Same, but Different: Algorithmic Diversification of Viewpoints in News. / Tintarev, Nava; Sullivan, Emily; Guldin, Dror et al.
UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. New York, NY: Association for Computing Machinery (ACM), 2018. p. 7-13.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Same, Same, but Different

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AU - Sullivan, Emily

AU - Guldin, Dror

AU - Qiu, Sihang

AU - Odjik, Daan

N1 - Conference code: 26

PY - 2018

Y1 - 2018

N2 - Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.

AB - Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.

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KW - diversity based ranking

KW - framing

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M3 - Conference contribution

SN - 978-1-4503-5784-5

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PB - Association for Computing Machinery (ACM)

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ER -

Same, Same, but Different: Algorithmic Diversification of Viewpoints in News

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this